Data assimilation (DA) refers to a family of methods used to synchronize a dynamical model to sparse or noisy measurements of model states. In this paper, we propose a wearable DA platform for neurological research and report our progress in translating a DA computational framework from desktop computation to embedded computation. The unscented Kalman filter (UKF) and a neural mass model (NMM) for sleep-wake regulation are introduced. Next, selection of suitable UKF parameters through MATLAB simulations is described. Finally, four variations of the DA framework are run on an embedded microprocessor in order to find the variation that minimizes computation time while maintaining state reconstruction accuracy. By reducing computational precision of the equation integrator and using a piecewise-linear approximation in place of the tanh function, we increased computational speed by a factor of 3.6 while maintaining a high level of state reconstruction fidelity.